Recent research has highlighted the role of relational inductive biases in building learning agents that can generalize and reason in a compositional manner. However, while relational learning algorithms such as graph neural networks (GNNs) show promise, we do not understand how effectively these approaches can adapt to new tasks. In this work, we study the task of logical generalization using GNNs by designing a benchmark suite grounded in first-order logic. Our benchmark suite, GraphLog, requires that learning algorithms perform rule induction in different synthetic logics, represented as knowledge graphs. GraphLog consists of relation prediction tasks on 57 distinct logical domains. We use GraphLog to evaluate GNNs in three different setups: single-task supervised learning, multi-task pretraining, and continual learning. Unlike previous benchmarks, our approach allows us to precisely control the logical relationship between the different tasks. We find that the ability for models to generalize and adapt is strongly determined by the diversity of the logical rules they encounter during training, and our results highlight new challenges for the design of GNN models. We publicly release the dataset and code used to generate and interact with the dataset at https://www.cs.mcgill.ca/~ksinha4/graphlog.

May 12 - 13, 2020 - Mayo Civic Center - Rochester, Minnesota Mayo Clinic, driven by its values, adopts a future-forward approach to leading health care transformation. Leveraging medical excellence and digital health sciences, artificial intelligence plays a critical role. The Mayo Clinic Artificial Intelligence Symposium aims to bring the health care AI community together to learn about current activities, share best practices, and foster collaborations toward digital health and medicine. We are currently accepting abstracts for the Artificial Intelligence Symposium 2020. Proposals will be reviewed and confirmed on a first come, first serve basis.

Micron has acquired FWDNXT, a machine learning software and hardware startup that spun out of Purdue. Micron is integrating FWDNXT's artificial intelligence hardware and software technology with its advanced memory to explore deep learning solutions for data analytics, particularly in IoT and edge computing. Purdue provided the entrepreneurial resources to help me achieve my vision of taking our work on machine learning and deep learning technology to a much wider audience where we can have a bigger impact," said Eugenio Culurciello, Micron fellow and chief machine learning architect. "Micron has the leadership in memory, long history of innovation and drive to deliver power and performance capabilities that address the most complex and demanding edge applications at scale." Culurciello founded FWDNXT while working as an associate professor in Purdue's College of Engineering. Based in the Purdue Research Park, FWDNXT designed next-generation hardware and software for deep learning aimed at enabling computers to understand the world in the same way humans do. Culurciello worked closely with the Purdue Research Foundation Office of Technology Commercialization to secure and develop an intellectual property rights strategy for the AI technology that he developed at Purdue, which Micron licenses today. The FWDNXT acquisition is another strong show of confidence by industry in Purdue technology designed to make a difference for Indiana and beyond," said Brooke Beier, vice president of the Office of Technology Commercialization.

Dr. Truby is Director of the Centre for Law & Development at Qatar University College of Law, alegal research and policy centre focused on delivering solutions to the needs of Qatar's National Development Strategy. Its current research and roundtable agenda focuses upon financial innovation for Qatar's economic diversification, including artificial intelligence, cybersecurity, digital currencies and blockchain technology. As a lawyer and academic established in law, policy and social sciences, he has secured major research grants from Qatar Foundation as well as other corporate and public sponsors, enabling him to research and publish in areas of interest including financial innovation and regulation, cybersecurity, AML/CFT, taxation and commercial law. He also studies policy tools to impact social behavior towards to achieve decarbonization and other sustainability objectives to mitigate climate change. Before joining QU College of Law in 2010, Dr. Truby taught graduate and undergraduate courses on the LLM and LLB courses at Newcastle Law School (England).

'As a result of federal initiatives to "get tough on crime," such as the Reagan Administration's War on Drugs and the Clinton Administration's "Three Strikes" laws, the total number of incarcerated Americans more than quadrupled from roughly 500,000 inmates in 1980 to 2.2 million inmates in 2015. During these decades, black Americans were incarcerated at a rate five times higher than that of white Americans. Despite a new 2019 US Bureau of Justice Statistics (BJS) report, which suggests that the racial disparity between white and black incarceration rates is "narrowing," a Pew Research Center review of BJS stats reveals that this 2019 report "counts only inmates sentenced to more than a year."Moreover, Whites accounted for 64% of adults but 30% of prisoners. . . . In 2017, there were 1,549 black prisoners for every 100,000 black adults--nearly six times the imprisonment rate for whites (272 per 100,000)."

Did you know the TNW Conference has a track fully dedicated to exploring the latest work culture trends and the future of work this year? Check out the full program here. The most exciting breakthroughs of the twenty-first century will not occur because of technology, but because of an expanding concept of what it means to be human. Before we dive into why more women should lead AI teams, I want to share a fascinating story I heard from Tania Biland, a 3rd-year student of Lucerne University of Applied Sciences and Arts. After 4 weeks of work, each team had to present their work.

WEST LAFAYETTE, Ind. – Technology that combines machine learning with artificial intelligence from Purdue University has taken its next giant leap toward powering more Internet of Things and edge computing devices. FWDNXT, a software and hardware startup that spun out of Purdue, was acquired in October by Micron Technology Inc., an industry leader in innovative memory and storage solutions. Micron is integrating FWDNXT's artificial intelligence hardware and software technology with its advanced memory to explore deep learning solutions for data analytics, particularly in IoT and edge computing. "Purdue provided the entrepreneurial resources to help me achieve my vision of taking our work on machine learning and deep learning technology to a much wider audience where we can have a bigger impact," said Eugenio Culurciello, Micron fellow and chief machine learning architect. "Micron has the leadership in memory, long history of innovation and drive to deliver power and performance capabilities that address the most complex and demanding edge applications at scale."

Human gait refers to a daily motion that represents not only mobility, but it can also be used to identify the walker by either human observers or computers. Recent studies reveal that gait even conveys information about the walker's emotion. Individuals in different emotion states may show different gait patterns. The mapping between various emotions and gait patterns provides a new source for automated emotion recognition. Compared to traditional emotion detection biometrics, such as facial expression, speech and physiological parameters, gait is remotely observable, more difficult to imitate, and requires less cooperation from the subject. These advantages make gait a promising source for emotion detection. This article reviews current research on gait-based emotion detection, particularly on how gait parameters can be affected by different emotion states and how the emotion states can be recognized through distinct gait patterns. We focus on the detailed methods and techniques applied in the whole process of emotion recognition: data collection, preprocessing, and classification. At last, we discuss possible future developments of efficient and effective gait-based emotion recognition using the state of the art techniques on intelligent computation and big data.

With the advent of renewable energy resources, generation in power networks is drifting from the classical centralized paradigm to an increasingly distributed scenario. While offering many advantages, renewable-based generation can compromise grid reliability, due to its intermittent nature and creation of reverse power flows. In order to guarantee the safe operation of power systems and avoid dangerous phenomena like blackouts, innovative and efficient control algorithms are necessary. Nevertheless, advanced algorithms necessitate grid identification, that is, the knowledge of grid topology and line parameters. Most works on the identification of electric networks focus on topology verification, assuming a known initial topology and aiming at detecting sparse changes, such as line trips or switch activations [1, 2]. More recently, attention has shifted to the estimation of network topology and line parameters without any apriori information. Two main branches of research have appeared. On the one hand, works like [3, 4] propose learning algorithms that exploit the statistical properties of nodal measurements to determine the operational structure and the line impedances. These approaches have the major advantage of accounting for buses with no available measurements (hidden nodes) [4], although restrictive assumptions are required, e.g.

Learning-based approaches often outperform hand-coded algorithmic solutions for many problems in robotics. However, learning long-horizon tasks on real robot hardware can be intractable, and transferring a learned policy from simulation to reality is still extremely challenging. We present a novel approach to model-free reinforcement learning that can leverage existing sub-optimal solutions as an algorithmic prior during training and deployment. During training, our gated fusion approach enables the prior to guide the initial stages of exploration, increasing sample-efficiency and enabling learning from sparse long-horizon reward signals. Importantly, the policy can learn to improve beyond the performance of the sub-optimal prior since the prior's influence is annealed gradually. During deployment, the policy's uncertainty provides a reliable strategy for transferring a simulation-trained policy to the real world by falling back to the prior controller in uncertain states. We show the efficacy of our Multiplicative Controller Fusion approach on the task of robot navigation and demonstrate safe transfer from simulation to the real world without any fine tuning. The code for this project is made publicly available at https://sites.google.com/view/mcf-nav/home.